class_docstr: Manages a PyTorch model for training.
class_docstr_tok: ["Manages","a","PyTorch","model","for","training","."]
docstr: Evaluates the model on the validation data set.
docstr_tok: ["Evaluates","the","model","on","the","validation","data","set","."]
code: def predict(
      self, data_creator, batch_size=32, profile=False, callbacks=None
  ):
      """Evaluates the model on the validation data set."""
      config = copy.copy(self.config)
      self._toggle_profiling(profile=profile)
  
      partition = data_creator(config, batch_size)
      self.load_state_dict(self.state_dict.value)
      result = super().predict(
          partition=partition,
          batch_size=batch_size,
          profile=profile,
          callbacks=callbacks,
      )
      if self.log_to_driver:
          LogMonitor.stop_log_monitor(
              self.log_path, self.logger_thread, self.thread_stop
          )
      return result
  
file_path: BigDL/python/orca/src/bigdl/orca/learn/pytorch/pytorch_pyspark_worker.py
imports: from bigdl.orca.learn.pytorch.torch_runner import TorchRunner
  import torch.distributed
  import logging
  import os
  import tempfile
  import copy
  from pyspark import BarrierTaskContext, TaskContext
  from bigdl.orca.learn.utils import save_pkl, duplicate_stdout_stderr_to_file, get_rank, get_partition_id
  from bigdl.orca.learn.log_monitor import LogMonitor
label_desc: Use image node_id 6 for calling the PytorchPysparkWorker obj's underlying member method code with example usage: obj.predict(data_creator, batch_size, profile, callbacks) and returns: result